---
title: "HCAMP Cluster Comparison"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: scroll
theme: united
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(here)
library(janitor)
library(rio)
library(colorblindr)
library(gghighlight)
library(forcats)
library(ggrepel)
library(knitr)
library(kableExtra)
library(reactable)
library(plotly)
library(glue)
library(fs)
library(rstatix)
library(ggpubr)
library(writexl)
library(remotes)
library(profvis)
theme_set(theme_minimal(15) +
theme(legend.position = "bottom",
panel.grid.major.x = element_line(color = "gray60"),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank())
)
```
```{r global, include=FALSE}
#all clean sims data
sims_concussion_data <- read_csv(here("data", "sims_concussion_data.csv"))
sims_concussion_data <- sims_concussion_data %>%
mutate(age = as.factor(age))
#impact data only
one_impact_test <- read_csv(here("data", "one_post_injury_impact_test.csv"))
two_impact_test <- read_csv(here("data", "two_post_injury_impact_test.csv"))
three_impact_test <- read_csv(here("data", "three_post_injury_impact_test.csv"))
four_impact_test <- read_csv(here("data", "four_post_injury_impact_test.csv"))
# impact sims merge data
one_impact_sims <- read_csv(here("data", "one_post_injury_test_impact_sims_merge.csv"))
two_impact_sims <- read_csv(here("data", "two_post_injury_test_impact_sims_merge.csv"))
three_impact_sims <- read_csv(here("data", "three_post_injury_test_impact_sims_merge.csv"))
four_impact_sims <- read_csv(here("data", "four_post_injury_test_impact_sims_merge.csv"))
# pcss items
pcss_items <- import(here("data", "pcss_items.xlsx"),
setclass = "tbl_df")
pcss_items_data <- import(here("data", "pcss_items_data.xlsx"),
setclass = "tbl_df")
pcss_cluster <- import(here("data", "cluster_scores.xlsx"),
setclass = "tbl_df")
```
# Symptom Comparisons
Sidebar {.sidebar}
------------
Symptoms that appear to be rated with higher severity more consistently:
1. Headache
2. Difficulty concentrating
3. Difficulty remembering
4. Irritability
5. Light sensitivity
Symptoms from the headache-migraine, cognitive and anxiety-mood clusters appear to remain elevated throughout recovery.
The vestibular cluster symptom of dizziness appears to be rated highest at the time of first testing and lessen in severity across the recovery time.
```{r, include=FALSE}
one_impact_test_cluster <- one_impact_test %>%
pivot_longer(
cols = c(32:37),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
) %>%
select(c(1:8, 37, 38))
one_impact_test_symptoms <- one_impact_test %>%
pivot_longer(
cols = c(9:30),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(c(1:15, 21, 22))
tst_aov <- aov(total_symptom_score_post_injury_1 ~ post_injury_1_rating,
data = one_impact_test_symptoms)
summary(tst_aov)
one_impact_test_symptoms <- left_join(one_impact_test_symptoms, pcss_items_data)
```
```{r, include=FALSE}
two_impact_test_1 <- two_impact_test %>%
select(passport_id, gender, contains("_post_injury_1"))
two_impact_test_2 <- two_impact_test %>%
select(passport_id, gender, contains("_post_injury_2"))
two_impact_test_1_symptoms <- two_impact_test_1 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(-c(14:18))
two_impact_test_1_symptoms <- left_join(two_impact_test_1_symptoms, pcss_items_data)
two_impact_test_2_symptoms <- two_impact_test_2 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_2_rating",
names_pattern = "(.*)_post_injury_2"
) %>%
select(-c(14:18))
two_impact_test_2_symptoms <- left_join(two_impact_test_2_symptoms, pcss_items_data)
```
```{r, include=FALSE}
three_impact_test_1 <- three_impact_test %>%
select(passport_id, gender, contains("_post_injury_1"))
three_impact_test_2 <- three_impact_test %>%
select(passport_id, gender, contains("_post_injury_2"))
three_impact_test_3 <- three_impact_test %>%
select(passport_id, gender, contains("_post_injury_3"))
three_impact_test_1_symptoms <- three_impact_test_1 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(-c(14:18))
three_impact_test_1_symptoms <- left_join(three_impact_test_1_symptoms, pcss_items_data)
three_impact_test_2_symptoms <- three_impact_test_2 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_2_rating",
names_pattern = "(.*)_post_injury_2"
) %>%
select(-c(14:18))
three_impact_test_2_symptoms <- left_join(three_impact_test_2_symptoms, pcss_items_data)
three_impact_test_3_symptoms <- three_impact_test_3 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_3_rating",
names_pattern = "(.*)_post_injury_3"
) %>%
select(-c(14:18))
three_impact_test_3_symptoms <- left_join(three_impact_test_3_symptoms, pcss_items_data)
```
```{r, include=FALSE}
four_impact_test_1 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_1"))
four_impact_test_2 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_2"))
four_impact_test_3 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_3"))
four_impact_test_4 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_4"))
four_impact_test_1_symptoms <- four_impact_test_1 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(-c(14:18))
four_impact_test_1_symptoms <- left_join(four_impact_test_1_symptoms, pcss_items_data)
four_impact_test_2_symptoms <- four_impact_test_2 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_2_rating",
names_pattern = "(.*)_post_injury_2"
) %>%
select(-c(14:18))
four_impact_test_2_symptoms <- left_join(four_impact_test_2_symptoms, pcss_items_data)
four_impact_test_3_symptoms <- four_impact_test_3 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_3_rating",
names_pattern = "(.*)_post_injury_3"
) %>%
select(-c(14:18))
four_impact_test_3_symptoms <- left_join(four_impact_test_3_symptoms, pcss_items_data)
four_impact_test_4_symptoms <- four_impact_test_4 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_4_rating",
names_pattern = "(.*)_post_injury_4"
) %>%
select(-c(14:18))
four_impact_test_4_symptoms <- left_join(four_impact_test_4_symptoms, pcss_items_data)
```
Row {.tabset}
-----------------------------------------------------------------------
### One Test
```{r, include=FALSE}
all_symptom_tbl <- one_impact_test_symptoms %>%
group_by(gender, cluster, symptom) %>%
summarize(Mean = mean(post_injury_1_rating),
SD = sd(post_injury_1_rating),
Min = min(post_injury_1_rating),
Max = max(post_injury_1_rating),
Total = length(post_injury_1_rating)) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
cluster = colDef(name = "Symptom Cluster"),
symptom = colDef(name = "Symptom"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
symptom_tbl <- function(df, a, b, c, d) {
df %>%
group_by({{a}}, {{b}}, {{c}}) %>%
summarize(Mean = mean({{d}}),
SD = sd({{d}}),
Min = min({{d}}),
Max = max({{d}}),
Total = length({{d}})) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
cluster = colDef(name = "Symptom Cluster"),
symptom = colDef(name = "Symptom"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
symptom_tbl_over_three <- function(df, a, b, c, d) {
df %>%
group_by({{a}}, {{b}}, {{c}}) %>%
filter({{d}} >=3) %>%
summarize(Mean = mean({{d}}),
SD = sd({{d}}),
Min = min({{d}}),
Max = max({{d}}),
Total = length({{d}})) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
cluster = colDef(name = "Symptom Cluster"),
symptom = colDef(name = "Symptom"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
```
```{r, include=TRUE}
symptom_tbl(one_impact_test_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
Row {.tabset}
-----------------------------------------------------------------------
### Two Tests - Test 1
```{r, include=TRUE}
symptom_tbl(two_impact_test_1_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
### Two Tests - Test 2
```{r, include=TRUE}
symptom_tbl(two_impact_test_2_symptoms, gender, cluster, symptom, post_injury_2_rating)
```
Row {.tabset}
-----------------------------------------------------------------------
### Three Tests - Test 1
```{r, include=TRUE}
symptom_tbl(three_impact_test_1_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
### Three Tests - Test 2
```{r, include=TRUE}
symptom_tbl(three_impact_test_2_symptoms, gender, cluster, symptom, post_injury_2_rating)
```
### Three Tests - Test 3
```{r, include=TRUE}
symptom_tbl(three_impact_test_3_symptoms, gender, cluster, symptom, post_injury_3_rating)
```
Row {.tabset}
-----------------------------------------------------------------------
### Four Tests - Test 1
```{r, include=TRUE}
symptom_tbl(four_impact_test_1_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
### Four Tests - Test 2
```{r, include=TRUE}
symptom_tbl(four_impact_test_2_symptoms, gender, cluster, symptom, post_injury_2_rating)
```
### Four Tests - Test 3
```{r, include=TRUE}
symptom_tbl(four_impact_test_3_symptoms, gender, cluster, symptom, post_injury_3_rating)
```
### Four Tests - Test 4
```{r, include=TRUE}
symptom_tbl(four_impact_test_4_symptoms, gender, cluster, symptom, post_injury_4_rating)
```
# Cluster Comparisons
Sidebar {.sidebar}
------------
Symptom Cluster Potential Symptom Severity Range:
1. Headache-Migraine Cluster: 0 - 18 (3 symptoms)
2. Cognitive Cluster: 0 - 24 (4 symptoms)
3. Anxiety-Mood Cluster: 0 - 30 (5 symptoms)
4. Ocular-Motor Cluster: 0 - 6 (1 symptom)
5. Vestibular Cluster: 0 - 24 (4 symptoms)
6. Sleep Cluster: 0 - 30 (5 symptoms)
To compare symptom cluster severity scores, each cluster was rescaled for the distribution of scores to fall between 0 to 1. Each cluster was rescaled individually and then grouped together into the same variable corresponding to post-injury test to compare with one-way ANOVAs.
**One Test Summary**
* Non-significant cluster comparisons: anxiety-mood/ocular-motor and cognitive/sleep
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
* Cognitive and sleep clusters tied for second highest rated clusters
* Anxiety-mood and ocular-motor rated the lowest
**Two Tests Summary**
_Test One:_
* Non-significant cluster comparisons: cognitive/sleep
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
* Cognitive and sleep clusters tied for second highest rated clusters
* Anxiety-mood and ocular-motor rated the lowest
_Test Two:_
* Non-significant cluster comparisons: anxiety-mood/ocular-motor, cognitive/sleep, anxiety-mood/vestibular, and ocular-motor/vestibular
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
**Three Tests Summary**
_Test One:_
* Non-significant cluster comparisons: anxiety-mood/vestibular
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
_Test Two:_
* Non-significant cluster comparisons: anxiety-mood/ocular-motor, cognitive/sleep, anxiety-mood/vestibular, and ocular-motor/vestibular
* Headache-migraine cluster significantly higher than all other clusters
_Test Three:_
* Non-significant cluster comparisons: cognitive/headache-migraine, anxiety-mood/ocular-motor, cognitive/sleep, headache-migraine/sleep, anxiety-mood/vestibular, and ocular-motor/vestibular
* Headache-migraine, cognitive, and sleep clusters significantly higher than other clusters
**Four Tests Summary**
_Test One:_
* Non-significant cluster comparisons: anxiety-mood/ocular-motor, cognitive/sleep, cognitive/vestibular, and sleep/vestibular
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
* Cognitive, vestibular, and sleep clusters not significantly different than each other and represent second set of clusters behind headache-migraine
_Test Two:_
* Non-significant cluster comparisons: anxiety-mood/ocular-motor, cognitive/sleep, and ocular-motor/vestibular
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
_Test Three:_
* Non-significant cluster comparisons: cognitive/headache-migraine, anxiety-mood/ocular-motor, cognitive/sleep, anxiety-mood/vestibular, and ocular-motor/vestibular
* All other PWC were significant
* Headache-migraine cluster rated the highest but not significantly different than cognitive cluster
* Cognitive cluster rated second highest but not significantly different than sleep cluster
_Test Four:_
* Non-significant cluster comparisons: anxiety-mood/cognitive, cognitive/headache-migraine, anxiety-mood/ocular-motor, cognitive/ocular-motor, anxiety-mood/sleep, cognitive/sleep, headache-migraine/sleep, ocular-motor/sleep, anxiety-mood/vestibular, ocular-motor/vestibular, and sleep/vestibular
* Significant cluster comparisons: cognitive/vestibular, headache-migraine/vestibular, anxiety-mood/headache-migraine, ocular-motor/headache-migraine
* Probably most important comparison from this test was the significant difference between anxiety-mood and headache-migraine severity ratings
Row {.tabset}
-----------------------------------------------------------------------
### 1 Test Cluster Summary
```{r, include=FALSE}
clusters_one_impact_test <- one_impact_test %>%
select(c(1:8, 32:37)) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age = as.character(age))
str(clusters_one_impact_test)
# normalize <- function(x) {
# return ((x - min(x)) / (max(x) - min(x)))
# }
rescale01b <- function(x) {
if(is.numeric(x)) {
z <- na.omit(x)
min_z <- min(z)
max_z <- max(z)
(z - min_z) / (max_z - min_z)
}
else {
return()
}
}
rescale01b(c(0, 5, 10))
clusters_one_impact_test_scaled <- map_df(clusters_one_impact_test, rescale01b)
clusters_one_impact_test_scaled <- bind_cols(clusters_one_impact_test,
clusters_one_impact_test_scaled)
clusters_one_impact_test_scaled <- clusters_one_impact_test_scaled %>%
select(-c(9:14))
clusters_one_impact_test_scaled[sapply(clusters_one_impact_test_scaled, is.character)] <- lapply(clusters_one_impact_test_scaled[sapply(clusters_one_impact_test_scaled, is.character)], as.factor)
str(clusters_one_impact_test_scaled)
clusters_one_impact_test_scaled_long <- clusters_one_impact_test_scaled %>%
pivot_longer(
cols = c(9:14),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
str(clusters_one_impact_test_scaled_long)
```
```{r, include=FALSE}
#summary
cluster_smry_tbl <- function(df, x) {
df %>%
group_by(symptom_cluster) %>%
summarize(mean = mean({{x}}),
sd = sd({{x}}),
min = min({{x}}),
max = max({{x}}),
total = length({{x}})) %>%
mutate(mean = as.numeric(mean),
sd = as.numeric(sd),
min = as.numeric(min),
max = as.numeric(max),
total = as.numeric(total)) %>%
reactable(
columns = list(
symptom_cluster = colDef(name = "Symptom Cluster",
align = "center"),
mean = colDef(name = "Mean",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
sd = colDef(name = "SD",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
min = colDef(name = "Min",
align = "center",
format = colFormat(separators = TRUE)),
max = colDef(name = "Max",
align = "center",
format = colFormat(separators = TRUE)),
total = colDef(name = "Total",
align = "center",
format = colFormat(separators = TRUE, suffix = " concussions"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
}
cluster_smry_tbl(clusters_one_impact_test_scaled_long, post_injury_1_score)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_one_impact_test_scaled_long, post_injury_1_score)
```
### 1 Test Cluster Boxplot
```{r, include=FALSE}
cluster_bxp_1 <- ggplot(clusters_one_impact_test_scaled_long,
aes(symptom_cluster, post_injury_1_score)) +
geom_boxplot() +
labs(x = "Symptom Cluster",
y = "Scaled Severity Score") +
theme(legend.position = "none",
axis.text.x = element_text(angle = 25))
cluster_bxp <- function(df, x, y) {
ggplot(df, aes({{x}}, {{y}})) +
geom_boxplot() +
labs(x = "Symptom Cluster",
y = "Scaled Severity Score") +
theme(legend.position = "none",
axis.text.x = element_text(angle = 25))
}
cluster_bxp(clusters_one_impact_test_scaled_long, symptom_cluster, post_injury_1_score)
```
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_one_impact_test_scaled_long, symptom_cluster, post_injury_1_score))
```
### 1 Test Cluster ANOVA
```{r, include=FALSE}
cluster_one_test_aov <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_one_impact_test_scaled_long)
summary(cluster_one_test_aov)
cluster_one_test_aov
```
```{r, include=TRUE}
summary(cluster_one_test_aov)
```
### 1 Test Cluster PWC
```{r, include=FALSE}
cluster_one_test_aov_pwc <- clusters_one_impact_test_scaled_long %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_one_test_aov_pwc <- cluster_one_test_aov_pwc %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_one_test_aov_pwc %>%
reactable()
```
Row {.tabset}
-----------------------------------------------------------------------
### 2 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_two_impact_test_1 <- two_impact_test %>%
select(c(1:12, contains("_post_injury_1"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2))
clusters_two_impact_test_1 <- clusters_two_impact_test_1 %>%
select(c(1:12, 36:41))
clusters_two_impact_test_1_scaled <- map_df(clusters_two_impact_test_1, rescale01b)
clusters_two_impact_test_1_scaled <- bind_cols(clusters_two_impact_test_1,
clusters_two_impact_test_1_scaled)
clusters_two_impact_test_1_scaled <- clusters_two_impact_test_1_scaled %>%
select(-c(13:18))
clusters_two_impact_test_1_scaled[sapply(clusters_two_impact_test_1_scaled, is.character)] <- lapply(clusters_two_impact_test_1_scaled[sapply(clusters_two_impact_test_1_scaled, is.character)], as.factor)
str(clusters_two_impact_test_1_scaled)
clusters_two_impact_test_1_long <- clusters_two_impact_test_1_scaled %>%
pivot_longer(
cols = c(13:18),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_two_impact_test_2 <- two_impact_test %>%
select(c(1:12, contains("_post_injury_2"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2))
clusters_two_impact_test_2 <- clusters_two_impact_test_2 %>%
select(c(1:12, 36:41))
clusters_two_impact_test_2_scaled <- map_df(clusters_two_impact_test_2, rescale01b)
clusters_two_impact_test_2_scaled <- bind_cols(clusters_two_impact_test_2,
clusters_two_impact_test_2_scaled)
clusters_two_impact_test_2_scaled <- clusters_two_impact_test_2_scaled %>%
select(-c(13:18))
clusters_two_impact_test_2_scaled[sapply(clusters_two_impact_test_2_scaled, is.character)] <- lapply(clusters_two_impact_test_2_scaled[sapply(clusters_two_impact_test_2_scaled, is.character)], as.factor)
str(clusters_two_impact_test_2_scaled)
clusters_two_impact_test_2_long <- clusters_two_impact_test_2_scaled %>%
pivot_longer(
cols = c(13:18),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
clusters_two_impact_test_complete <- left_join(clusters_two_impact_test_1_long,
clusters_two_impact_test_2_long)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_two_impact_test_complete, post_injury_1_score)
```
### 2 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster, post_injury_1_score))
```
### 2 Tests Cluster ANOVA - Test 1
```{r, include=FALSE}
cluster_two_tests_aov_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_two_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_two_tests_aov_1)
```
### 2 Tests Cluster PWC - Test 1
```{r, include=FALSE}
cluster_two_tests_aov_pwc_1 <- clusters_two_impact_test_complete %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_two_tests_aov_pwc_1 <- cluster_two_tests_aov_pwc_1 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_two_tests_aov_pwc_1 %>%
reactable()
```
### 2 Tests Cluster Summary - Test 2
```{r, include=TRUE}
cluster_smry_tbl(clusters_two_impact_test_complete, post_injury_2_score)
```
### 2 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster, post_injury_2_score))
```
### 2 Tests Cluster ANOVA - Test 2
```{r, include=FALSE}
cluster_two_tests_aov_2 <- aov(post_injury_2_score ~ symptom_cluster,
data = clusters_two_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_two_tests_aov_2)
```
### 2 Tests Cluster PWC - Test 2
```{r, include=FALSE}
cluster_two_tests_aov_pwc_2 <- clusters_two_impact_test_complete %>%
pairwise_t_test(
post_injury_2_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_two_tests_aov_pwc_2 <- cluster_two_tests_aov_pwc_2 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_two_tests_aov_pwc_2 %>%
reactable()
```
Row {.tabset}
-----------------------------------------------------------------------
### 3 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_three_impact_test_1 <- three_impact_test %>%
select(c(1:16, contains("_post_injury_1"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3))
clusters_three_impact_test_1 <- clusters_three_impact_test_1 %>%
select(c(1:16, 40:45))
clusters_three_impact_test_1_scaled <- map_df(clusters_three_impact_test_1, rescale01b)
clusters_three_impact_test_1_scaled <- bind_cols(clusters_three_impact_test_1,
clusters_three_impact_test_1_scaled)
clusters_three_impact_test_1_scaled <- clusters_three_impact_test_1_scaled %>%
select(-c(17:22))
clusters_three_impact_test_1_scaled[sapply(clusters_three_impact_test_1_scaled, is.character)] <- lapply(clusters_three_impact_test_1_scaled[sapply(clusters_three_impact_test_1_scaled, is.character)], as.factor)
str(clusters_three_impact_test_1_scaled)
clusters_three_impact_test_1_long <- clusters_three_impact_test_1_scaled %>%
pivot_longer(
cols = c(17:22),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_three_impact_test_2 <- three_impact_test %>%
select(c(1:16, contains("_post_injury_2"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3))
clusters_three_impact_test_2 <- clusters_three_impact_test_2 %>%
select(c(1:16, 40:45))
clusters_three_impact_test_2_scaled <- map_df(clusters_three_impact_test_2, rescale01b)
clusters_three_impact_test_2_scaled <- bind_cols(clusters_three_impact_test_2,
clusters_three_impact_test_2_scaled)
clusters_three_impact_test_2_scaled <- clusters_three_impact_test_2_scaled %>%
select(-c(17:22))
clusters_three_impact_test_2_scaled[sapply(clusters_three_impact_test_2_scaled, is.character)] <- lapply(clusters_three_impact_test_2_scaled[sapply(clusters_three_impact_test_2_scaled, is.character)], as.factor)
str(clusters_three_impact_test_2_scaled)
clusters_three_impact_test_2_long <- clusters_three_impact_test_2_scaled %>%
pivot_longer(
cols = c(17:22),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
clusters_three_impact_test_3 <- three_impact_test %>%
select(c(1:16, contains("_post_injury_3"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3))
clusters_three_impact_test_3 <- clusters_three_impact_test_3 %>%
select(c(1:16, 40:45))
clusters_three_impact_test_3_scaled <- map_df(clusters_three_impact_test_3, rescale01b)
clusters_three_impact_test_3_scaled <- bind_cols(clusters_three_impact_test_3,
clusters_three_impact_test_3_scaled)
clusters_three_impact_test_3_scaled <- clusters_three_impact_test_3_scaled %>%
select(-c(17:22))
clusters_three_impact_test_3_scaled[sapply(clusters_three_impact_test_3_scaled, is.character)] <- lapply(clusters_three_impact_test_3_scaled[sapply(clusters_three_impact_test_3_scaled, is.character)], as.factor)
str(clusters_three_impact_test_3_scaled)
clusters_three_impact_test_3_long <- clusters_three_impact_test_3_scaled %>%
pivot_longer(
cols = c(17:22),
names_to = "symptom_cluster",
values_to = "post_injury_3_score",
names_pattern = "(.*)_score_post_injury_3"
)
clusters_three_impact_test_join_1 <- left_join(clusters_three_impact_test_1_long,
clusters_three_impact_test_2_long)
clusters_three_impact_test_complete <- left_join(clusters_three_impact_test_join_1,
clusters_three_impact_test_3_long)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_three_impact_test_complete, post_injury_1_score)
```
### 3 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster, post_injury_1_score))
```
### 3 Tests Cluster ANOVA - Test 1
```{r, include=FALSE}
cluster_three_tests_aov_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_three_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_three_tests_aov_1)
```
### 3 Tests Cluster PWC - Test 1
```{r, include=FALSE}
cluster_three_tests_aov_pwc_1 <- clusters_three_impact_test_complete %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_three_tests_aov_pwc_1 <- cluster_three_tests_aov_pwc_1 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_three_tests_aov_pwc_1 %>%
reactable()
```
### 3 Tests Cluster Summary - Test 2
```{r, include=TRUE}
cluster_smry_tbl(clusters_three_impact_test_complete, post_injury_2_score)
```
### 3 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster, post_injury_2_score))
```
### 3 Tests Cluster ANOVA - Test 2
```{r, include=FALSE}
cluster_three_tests_aov_2 <- aov(post_injury_2_score ~ symptom_cluster,
data = clusters_three_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_three_tests_aov_2)
```
### 3 Tests Cluster PWC - Test 2
```{r, include=FALSE}
cluster_three_tests_aov_pwc_2 <- clusters_three_impact_test_complete %>%
pairwise_t_test(
post_injury_2_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_three_tests_aov_pwc_2 <- cluster_three_tests_aov_pwc_2 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_three_tests_aov_pwc_2 %>%
reactable()
```
### 3 Tests Cluster Summary - Test 3
```{r, include=TRUE}
cluster_smry_tbl(clusters_three_impact_test_complete, post_injury_3_score)
```
### 3 Tests Cluster Boxplot - Test 3
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster, post_injury_3_score))
```
### 3 Tests Cluster ANOVA - Test 3
```{r, include=FALSE}
cluster_three_tests_aov_3 <- aov(post_injury_3_score ~ symptom_cluster,
data = clusters_three_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_three_tests_aov_3)
```
### 3 Tests Cluster PWC - Test 3
```{r, include=FALSE}
cluster_three_tests_aov_pwc_3 <- clusters_three_impact_test_complete %>%
pairwise_t_test(
post_injury_3_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_three_tests_aov_pwc_3 <- cluster_three_tests_aov_pwc_3 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_three_tests_aov_pwc_3 %>%
reactable()
```
Row {.tabset}
-----------------------------------------------------------------------
### 4 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_four_impact_test_1 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_1"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_1 <- clusters_four_impact_test_1 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_1_scaled <- map_df(clusters_four_impact_test_1, rescale01b)
clusters_four_impact_test_1_scaled <- bind_cols(clusters_four_impact_test_1,
clusters_four_impact_test_1_scaled)
clusters_four_impact_test_1_scaled <- clusters_four_impact_test_1_scaled %>%
select(-c(21:26))
clusters_four_impact_test_1_scaled[sapply(clusters_four_impact_test_1_scaled, is.character)] <- lapply(clusters_four_impact_test_1_scaled[sapply(clusters_four_impact_test_1_scaled, is.character)], as.factor)
str(clusters_four_impact_test_1_scaled)
clusters_four_impact_test_1_long <- clusters_four_impact_test_1_scaled %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_four_impact_test_2 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_2"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_2 <- clusters_four_impact_test_2 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_2_scaled <- map_df(clusters_four_impact_test_2, rescale01b)
clusters_four_impact_test_2_scaled <- bind_cols(clusters_four_impact_test_2,
clusters_four_impact_test_2_scaled)
clusters_four_impact_test_2_scaled <- clusters_four_impact_test_2_scaled %>%
select(-c(21:26))
clusters_four_impact_test_2_scaled[sapply(clusters_four_impact_test_2_scaled, is.character)] <- lapply(clusters_four_impact_test_2_scaled[sapply(clusters_four_impact_test_2_scaled, is.character)], as.factor)
str(clusters_four_impact_test_2_scaled)
clusters_four_impact_test_2_long <- clusters_four_impact_test_2_scaled %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
clusters_four_impact_test_3 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_3"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_3 <- clusters_four_impact_test_3 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_3_scaled <- map_df(clusters_four_impact_test_3, rescale01b)
clusters_four_impact_test_3_scaled <- bind_cols(clusters_four_impact_test_3,
clusters_four_impact_test_3_scaled)
clusters_four_impact_test_3_scaled <- clusters_four_impact_test_3_scaled %>%
select(-c(21:26))
clusters_four_impact_test_3_scaled[sapply(clusters_four_impact_test_3_scaled, is.character)] <- lapply(clusters_four_impact_test_3_scaled[sapply(clusters_four_impact_test_3_scaled, is.character)], as.factor)
str(clusters_four_impact_test_3_scaled)
clusters_four_impact_test_3_long <- clusters_four_impact_test_3_scaled %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_3_score",
names_pattern = "(.*)_score_post_injury_3"
)
clusters_four_impact_test_4 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_4"))) %>%
mutate(X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_4 <- clusters_four_impact_test_4 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_4_scaled <- map_df(clusters_four_impact_test_4, rescale01b)
clusters_four_impact_test_4_scaled <- bind_cols(clusters_four_impact_test_4,
clusters_four_impact_test_4_scaled)
clusters_four_impact_test_4_scaled <- clusters_four_impact_test_4_scaled %>%
select(-c(21:26))
clusters_four_impact_test_4_scaled[sapply(clusters_four_impact_test_4_scaled, is.character)] <- lapply(clusters_four_impact_test_4_scaled[sapply(clusters_four_impact_test_4_scaled, is.character)], as.factor)
str(clusters_four_impact_test_4_scaled)
clusters_four_impact_test_4_long <- clusters_four_impact_test_4_scaled %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_4_score",
names_pattern = "(.*)_score_post_injury_4"
)
clusters_four_impact_test_join_1 <- left_join(clusters_four_impact_test_1_long,
clusters_four_impact_test_2_long)
clusters_four_impact_test_join_2 <- left_join(clusters_four_impact_test_join_1,
clusters_four_impact_test_3_long)
clusters_four_impact_test_complete <- left_join(clusters_four_impact_test_join_2,
clusters_four_impact_test_4_long)
str(clusters_four_impact_test_complete)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_1_score)
```
### 4 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_1_score))
```
### 4 Tests Cluster ANOVA - Test 1
```{r, include=FALSE}
cluster_four_tests_aov_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_1)
```
### 4 Tests Cluster PWC - Test 1
```{r, include=FALSE}
cluster_four_tests_aov_pwc_1 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_1 <- cluster_four_tests_aov_pwc_1 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_1 %>%
reactable()
```
### 4 Tests Cluster Summary - Test 2
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_2_score)
```
### 4 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_2_score))
```
### 4 Tests Cluster ANOVA - Test 2
```{r, include=FALSE}
cluster_four_tests_aov_2 <- aov(post_injury_2_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_2)
```
### 4 Tests Cluster PWC - Test 2
```{r, include=FALSE}
cluster_four_tests_aov_pwc_2 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_2_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_2 <- cluster_four_tests_aov_pwc_2 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_2 %>%
reactable()
```
### 4 Tests Cluster Summary - Test 3
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_3_score)
```
### 4 Tests Cluster Boxplot - Test 3
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_3_score))
```
### 4 Tests Cluster ANOVA - Test 3
```{r, include=FALSE}
cluster_four_tests_aov_3 <- aov(post_injury_3_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_3)
```
### 4 Tests Cluster PWC - Test 3
```{r, include=FALSE}
cluster_four_tests_aov_pwc_3 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_3_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_3 <- cluster_four_tests_aov_pwc_3 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_3 %>%
reactable()
```
### 4 Tests Cluster Summary - Test 4
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_4_score)
```
### 4 Tests Cluster Boxplot - Test 4
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_4_score))
```
### 4 Tests Cluster ANOVA - Test 4
```{r, include=FALSE}
cluster_four_tests_aov_4 <- aov(post_injury_4_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_4)
```
### 4 Tests Cluster PWC - Test 4
```{r, include=FALSE}
cluster_four_tests_aov_pwc_4 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_4_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_4 <- cluster_four_tests_aov_pwc_4 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_4 %>%
reactable()
```
# Gender-Cluster Interaction
Sidebar {.sidebar}
------------
Row {.tabset}
-----------------------------------------------------------------------
### 1 Test Cluster Summary
```{r, include=FALSE}
clusters_one_impact_test_scaled_long
scaled_cluster_gender_tbl <- function(df, a, b, c) {
df %>%
group_by({{a}}, {{b}}) %>%
summarize(Mean = mean({{c}}),
SD = sd({{c}}),
Min = min({{c}}),
Max = max({{c}})) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
symptom_cluster = colDef(name = "Symptom Cluster"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
scaled_cluster_gender_tbl(clusters_one_impact_test_scaled_long,
gender,
symptom_cluster,
post_injury_1_score)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_one_impact_test_scaled_long,
gender,
symptom_cluster,
post_injury_1_score)
```
### 1 Test Cluster Boxplot
```{r, include=FALSE}
ggplot(clusters_one_impact_test_scaled_long,
aes(symptom_cluster, post_injury_1_score, color = gender)) +
geom_boxplot() +
scale_color_OkabeIto() +
coord_flip() +
labs(x = "",
y = "Scaled Severity Score") +
theme(legend.title = element_blank())
gender_cluster_bxp <- function(df, a, b, c) {
ggplot(df,
aes({{a}}, {{b}}, color = {{c}})) +
geom_boxplot() +
scale_color_OkabeIto() +
coord_flip() +
labs(x = "",
y = "Scaled Severity Score") +
theme(legend.title = element_blank())
}
gender_cluster_bxp(clusters_one_impact_test_scaled_long,
symptom_cluster,
post_injury_1_score,
gender)
```
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_one_impact_test_scaled_long,
symptom_cluster,
post_injury_1_score,
gender)
```
### 1 Test Cluster Two-Way ANOVA
```{r, include=FALSE}
cluster_one_test_aov2 <- aov(post_injury_1_score ~ symptom_cluster * gender,
data = clusters_one_impact_test_scaled_long)
library(car)
summary(cluster_one_test_aov2)
cluster_one_test_aov2
Anova(cluster_one_test_aov2, type = "III")
TukeyHSD(cluster_one_test_aov2)
TukeyHSD(cluster_one_test_aov2, which = "gender")
TukeyHSD(cluster_one_test_aov2, which = "symptom_cluster")
library(broom)
res_1 <- TukeyHSD(cluster_one_test_aov2)
tidy(res_1) %>%
reactable()
a_1 <- Anova(cluster_one_test_aov2, type = "III")
reactable(a_1)
```
```{r, include=TRUE}
reactable(a_1)
```
### 1 Test Cluster PWC
```{r, include=TRUE}
tidy(res_1) %>%
reactable()
```
Row {.tabset}
-----------------------------------------------------------------------
### 2 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_two_impact_test_complete[sapply(clusters_two_impact_test_complete, is.character)] <- lapply(clusters_two_impact_test_complete[sapply(clusters_two_impact_test_complete, is.character)], as.factor)
str(clusters_two_impact_test_complete)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_two_impact_test_complete,
gender,
symptom_cluster,
post_injury_1_score)
```
### 2 Tests Cluster Boxplot - Test 1
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster,
post_injury_1_score,
gender)
```
### 2 Tests Cluster Two-Way ANOVA - Test 1
```{r, include=FALSE}
cluster_two_tests_aov2_1 <- aov(post_injury_1_score ~ symptom_cluster * gender,
data = clusters_two_impact_test_complete)
summary(cluster_two_tests_aov2_1)
cluster_two_tests_aov2_1
Anova(cluster_two_tests_aov2_1, type = "III")
TukeyHSD(cluster_two_tests_aov2_1)
res_2_1 <- TukeyHSD(cluster_two_tests_aov2_1)
a_2_1 <- Anova(cluster_two_tests_aov2_1, type = "III")
```
```{r, include=TRUE}
reactable(a_2_1)
```
### 2 Tests Cluster PWC - Test 1
```{r, include=TRUE}
tidy(res_2_1) %>%
reactable()
```
### 2 Tests Cluster Summary - Test 2
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_two_impact_test_complete,
gender,
symptom_cluster,
post_injury_2_score)
```
### 2 Tests Cluster Boxplot - Test 2
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster,
post_injury_2_score,
gender)
```
### 2 Tests Cluster Two-Way ANOVA - Test 2
```{r, include=FALSE}
cluster_two_tests_aov2_2 <- aov(post_injury_2_score ~ symptom_cluster * gender,
data = clusters_two_impact_test_complete)
summary(cluster_two_tests_aov2_2)
cluster_two_tests_aov2_2
Anova(cluster_two_tests_aov2_2, type = "III")
TukeyHSD(cluster_two_tests_aov2_2)
res_2_2 <- TukeyHSD(cluster_two_tests_aov2_2)
a_2_2 <- Anova(cluster_two_tests_aov2_2, type = "III")
```
```{r, include=TRUE}
reactable(a_2_2)
```
### 2 Tests Cluster PWC - Test 1
```{r, include=TRUE}
tidy(res_2_2) %>%
reactable()
```
Row {.tabset}
-----------------------------------------------------------------------
### 3 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_three_impact_test_complete[sapply(clusters_three_impact_test_complete, is.character)] <- lapply(clusters_three_impact_test_complete[sapply(clusters_three_impact_test_complete, is.character)], as.factor)
str(clusters_three_impact_test_complete)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_three_impact_test_complete,
gender,
symptom_cluster,
post_injury_1_score)
```
### 3 Tests Cluster Boxplot - Test 1
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster,
post_injury_1_score,
gender)
```
### 3 Tests Cluster Two-Way ANOVA - Test 1
```{r, include=FALSE}
cluster_three_tests_aov2_1 <- aov(post_injury_1_score ~ symptom_cluster * gender,
data = clusters_three_impact_test_complete)
summary(cluster_three_tests_aov2_1)
cluster_three_tests_aov2_1
Anova(cluster_three_tests_aov2_1, type = "III")
TukeyHSD(cluster_three_tests_aov2_1)
res_3_1 <- TukeyHSD(cluster_three_tests_aov2_1)
a_3_1 <- Anova(cluster_three_tests_aov2_1, type = "III")
```
```{r, include=TRUE}
reactable(a_3_1)
```
### 3 Tests Cluster PWC - Test 1
```{r, include=TRUE}
tidy(res_3_1) %>%
reactable()
```
### 3 Tests Cluster Summary - Test 2
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_three_impact_test_complete,
gender,
symptom_cluster,
post_injury_2_score)
```
### 3 Tests Cluster Boxplot - Test 2
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster,
post_injury_2_score,
gender)
```
### 3 Tests Cluster Two-Way ANOVA - Test 2
```{r, include=FALSE}
cluster_three_tests_aov2_2 <- aov(post_injury_2_score ~ symptom_cluster * gender,
data = clusters_three_impact_test_complete)
summary(cluster_three_tests_aov2_2)
cluster_three_tests_aov2_2
Anova(cluster_three_tests_aov2_2, type = "III")
TukeyHSD(cluster_three_tests_aov2_2)
res_3_2 <- TukeyHSD(cluster_three_tests_aov2_2)
a_3_2 <- Anova(cluster_three_tests_aov2_2, type = "III")
```
```{r, include=TRUE}
reactable(a_3_2)
```
### 3 Tests Cluster PWC - Test 2
```{r, include=TRUE}
tidy(res_3_2) %>%
reactable()
```
### 3 Tests Cluster Summary - Test 3
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_three_impact_test_complete,
gender,
symptom_cluster,
post_injury_3_score)
```
### 3 Tests Cluster Boxplot - Test 3
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster,
post_injury_3_score,
gender)
```
### 3 Tests Cluster Two-Way ANOVA - Test 3
```{r, include=FALSE}
cluster_three_tests_aov2_3 <- aov(post_injury_3_score ~ symptom_cluster * gender,
data = clusters_three_impact_test_complete)
summary(cluster_three_tests_aov2_3)
cluster_three_tests_aov2_3
Anova(cluster_three_tests_aov2_3, type = "III")
TukeyHSD(cluster_three_tests_aov2_3)
res_3_3 <- TukeyHSD(cluster_three_tests_aov2_3)
a_3_3 <- Anova(cluster_three_tests_aov2_3, type = "III")
```
```{r, include=TRUE}
reactable(a_3_3)
```
### 3 Tests Cluster PWC - Test 3
```{r, include=TRUE}
tidy(res_3_3) %>%
reactable()
```
Row {.tabset}
-----------------------------------------------------------------------
### 4 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_four_impact_test_complete[sapply(clusters_four_impact_test_complete, is.character)] <- lapply(clusters_four_impact_test_complete[sapply(clusters_four_impact_test_complete, is.character)], as.factor)
str(clusters_four_impact_test_complete)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_four_impact_test_complete,
gender,
symptom_cluster,
post_injury_1_score)
```
### 4 Tests Cluster Boxplot - Test 1
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster,
post_injury_1_score,
gender)
```
### 4 Tests Cluster Two-Way ANOVA - Test 1
```{r, include=FALSE}
cluster_four_tests_aov2_1 <- aov(post_injury_1_score ~ symptom_cluster * gender,
data = clusters_four_impact_test_complete)
summary(cluster_four_tests_aov2_1)
cluster_four_tests_aov2_1
Anova(cluster_four_tests_aov2_1, type = "III")
TukeyHSD(cluster_four_tests_aov2_1)
res_4_1 <- TukeyHSD(cluster_four_tests_aov2_1)
a_4_1 <- Anova(cluster_four_tests_aov2_1, type = "III")
```
```{r, include=TRUE}
reactable(a_4_1)
```
### 4 Tests Cluster PWC - Test 1
```{r, include=TRUE}
tidy(res_4_1) %>%
reactable()
```
### 4 Tests Cluster Summary - Test 2
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_four_impact_test_complete,
gender,
symptom_cluster,
post_injury_2_score)
```
### 4 Tests Cluster Boxplot - Test 2
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster,
post_injury_2_score,
gender)
```
### 4 Tests Cluster Two-Way ANOVA - Test 2
```{r, include=FALSE}
cluster_four_tests_aov2_2 <- aov(post_injury_2_score ~ symptom_cluster * gender,
data = clusters_four_impact_test_complete)
summary(cluster_four_tests_aov2_2)
cluster_four_tests_aov2_2
Anova(cluster_four_tests_aov2_2, type = "III")
TukeyHSD(cluster_four_tests_aov2_2)
res_4_2 <- TukeyHSD(cluster_four_tests_aov2_2)
a_4_2 <- Anova(cluster_four_tests_aov2_2, type = "III")
```
```{r, include=TRUE}
reactable(a_4_2)
```
### 4 Tests Cluster PWC - Test 2
```{r, include=TRUE}
tidy(res_4_2) %>%
reactable()
```
### 4 Tests Cluster Summary - Test 3
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_four_impact_test_complete,
gender,
symptom_cluster,
post_injury_3_score)
```
### 4 Tests Cluster Boxplot - Test 3
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster,
post_injury_3_score,
gender)
```
### 4 Tests Cluster Two-Way ANOVA - Test 3
```{r, include=FALSE}
cluster_four_tests_aov2_3 <- aov(post_injury_3_score ~ symptom_cluster * gender,
data = clusters_four_impact_test_complete)
summary(cluster_four_tests_aov2_3)
cluster_four_tests_aov2_3
Anova(cluster_four_tests_aov2_3, type = "III")
TukeyHSD(cluster_four_tests_aov2_3)
res_4_3 <- TukeyHSD(cluster_four_tests_aov2_3)
a_4_3 <- Anova(cluster_four_tests_aov2_3, type = "III")
```
```{r, include=TRUE}
reactable(a_4_3)
```
### 4 Tests Cluster PWC - Test 3
```{r, include=TRUE}
tidy(res_4_3) %>%
reactable()
```
### 4 Tests Cluster Summary - Test 4
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_four_impact_test_complete,
gender,
symptom_cluster,
post_injury_4_score)
```
### 4 Tests Cluster Boxplot - Test 4
```{r, include=TRUE, fig.width=10}
gender_cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster,
post_injury_4_score,
gender)
```
### 4 Tests Cluster Two-Way ANOVA - Test 4
```{r, include=FALSE}
cluster_four_tests_aov2_4 <- aov(post_injury_4_score ~ symptom_cluster * gender,
data = clusters_four_impact_test_complete)
summary(cluster_four_tests_aov2_4)
cluster_four_tests_aov2_4
Anova(cluster_four_tests_aov2_4, type = "III")
TukeyHSD(cluster_four_tests_aov2_4)
res_4_4 <- TukeyHSD(cluster_four_tests_aov2_4)
a_4_4 <- Anova(cluster_four_tests_aov2_4, type = "III")
```
```{r, include=TRUE}
reactable(a_4_4)
```
### 4 Tests Cluster PWC - Test 4
```{r, include=TRUE}
tidy(res_4_4) %>%
reactable()
```
